###### Course Overview

## Predictive Modeling Training

**What is Predictive Modeling**

Predictive modeling is the process of creating, testing and validating a model. It uses statistics to predict the outcomes. Predictive modeling has different methods like machine learning, artificial intelligence and others. This model is made up of number of predictors which are likely to affect the future results. Predictive modeling is most widely used in information technology.

**Uses of Predictive Modeling**

Predictive modeling is the most commonly used statistical technique to predict the future behaviour. Predictive modeling analyzes the past performance to predict the future behaviour.

**Features in Predictive Modeling**

- Data Analysis and Manipulation
- Visualization
- Statistics
- Hypothesis Testing

**Pre requisites for taking this course**

The pre requisites for this course includes a basic statistical knowledge and details on software like SPSS or SAS or STATA.

**Target Audience for this course**

This course is more suitable for students or researchers who are interested in learning about predictive analytics.

**Predictive Modeling Course Objectives**

After the completion of this course you will be able to

- Understand how to use predictive analytics tools to solve real time business problems
- Learn about predictive models like regression, clustering and others
- Use predictive analytics techniques to interpret model outputs

## Predictive Modeling Course Description

**Section 1: Introduction**

__What is Predictive Modeling__

Predictive analytics is an emerging strategy across many business sectors and they are used to improve the performance of the companies. Predictive modeling is a part of predictive analytics which is used to create a statistical model to predict the future behaviour. The predictive modeling can be used on any type of event regardless of its occurrence. The predictive model to be used for a particular situation is often selected on the basis of the detection theory. This chapter includes an overview of predictive analytics and predictive modeling. This chapter also includes examples of predictive modeling.

__How to Build a Predictive Model__

The predictive models are used to analyze the past performance to predict the future results. There are several steps involved in building a predictive model

- Pre Processing
- Data Mining
- Results validation
- Understand business and data
- Prepare data
- Model data
- Evaluation
- Deployment
- Monitor and improve

All these steps are explained in detail under this chapter

**Section 2: Variables**

__Types of Variables__

There are different types of variables in predictive modeling. They are Predicator Variable, Numeric variable, ID variable, Factor variable, categorical variable, extraneous variable, confounding variable and Target Variable.

__Difference Between Variables__

The difference between each variables is explained in this chapter.

__Other Types – Extraneous Variables__

Extraneous variables are the ones that are not included in your test or experiment by you but it affects your experiment when you run your experiment. For example if you are running an experiment to find out whether one variable has an effect on the other variable but unfortunately you find another variable that is affecting the result of your outcome. These undesired variables are called extraneous variables.

**Section 3: Steps Included**

__How to Build a Predictive Model Steps__

This chapter contains the basic seven steps involved in predictive modeling

- Defining the objective – This section deals with the ways to define the objective of predictive models with relation to the goals of the business.
- Gathering the data -Collecting data from various sources is another important step in building predictive model. Examples are provided for collection of different types of data from various sources.
- Preparing the data for modelling – This section deals with the segregation of data and how determines how they can be used in predictive modeling.
- Selecting and transforming the variables – This topic deals with the steps for transformation of independent variables to best fit the dependent variable.
- Processing and evaluating the model – In this chapter you will go through several methods of processing and evaluating the model
- Validating the model – The predictive models should perform well on the data. This chapter deals with three powerful methods for ensuring the model fit.
- Implementing and maintaining the model – Effective implementation of Predictive model is another important step. This chapter discusses various auditing procedures and model maintenance practices

__Algorithms__

Algorithms perform data mining and statistical analysis to find out the trends in data. The predictive analytics has few built in algorithms like regression, time series, outliers, decision trees, k means and neural network.

__Forecasting Methods__

The forecasting methods used depend mainly on the type of data available. There are different methods of forecasting which are discussed in detail under this lesson

- Qualitative Forecasting
- Quantitative forecasting
- Cross sectional forecasting
- Time Series Forecasting

__What is Time Series__

Time series algorithms are used to perform time based predictions. The examples are smoothing methods which are discussed in the next chapter.

Time series data are useful to forecast something that is constantly changing over time like profit, share price and others. Forecasting the time series data will help to predict the sequence of observations in the future.

**Section 4: Smoothing Methods**

__Smoothing Methods – Moving Averages__

In moving average smoothing each observation is assigned an equal weight and each observation is forecasted using the average of the previous observations. This method is useful when the item to be forecasted remains unchanged over time. The formula for moving average method is also explained in this chapter.

__Smoothing Methods – Double Exponential Smoothing__

Exponential smoothing is one of the successful and most widely used forecasting methods. The forecasts that are produced using exponential smoothing methods are weighted averages of the previous observations. There are two types of exponential smoothing – Simple Exponential Smoothing and Double Exponential Smoothing.

Double exponential smoothing is the recursive application of an exponential filter twice in a time series. This should not be used if the data is expected to be affected by seasonality. This is used only when a trend in inherent in the dataset. The double exponential smoothing is explained in detail in this chapter with its formula and examples.

**Section 5: Regression Algorithms**

__Regression Algorithms – Exponential__

There are different types of statistical, data mining and machine learning algorithms in Predictive Modeling. Each algorithm is used to address the specific needs of the business concern. So choosing the right algorithm for your business is a great task. Regression algorithm is one among them. Regression algorithm is used to forecast continuous data like credit scoring or predicting the next outcome of a time based event. For example regression algorithm can be used to predict the trend of a stock movement with its past prices.

There are seven types of regression which are explained in detail under this chapter. In predictive modeling, linear and logistic regressions are considered to be the most important ones.

- Linear Regression
- Logistic Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
- ElasticNet Regression

This chapter also lets you learn how to select the right regression model for your business.

**Section 6: Clustering Algorithms**

__Clustering Algorithms – Definition__

Clustering can be defined as division of data into groups with similar objects. The clustering technique is not used to predict the value of the target variable in clustering. The clustering algorithm is used to segment the whole data into homogeneous clusters. For example, clustering is used for customer segmentation. This algorithm segments the customers based on more variables which can never be done by humans. To make a cluster algorithm perfect, there should be more similarity within the clusters and more differences between the clusters. There are different type of clusters and each are explained in detail under this chapter.

__Clustering Algorithms – Fuzzy C Means Clustering__

Fuzzy C Means Clustering is one of the widely used clustering algorithm. This is a method of clustering which lets a single data to be present in two or more clusters. This algorithm is mostly used in pattern recognition. This algorithm is used for analysis based on distance between various input data points. The formula for Fuzzy C Means Clustering is explained in this chapter. Here you will also learn about the algorithm steps of Fuzzy C Means Clustering. This chapter also contains the comparative study of K means Cluster algorithm and Fuzzy C Means Cluster algorithm.

**Section 7: Neural Network Algorithm**

__Neural Network Algorithm__

The neural network algorithm is used for pattern recognition, find out the predictions and learn from the result. An example of neural network is human brain. Neural networks in data mining applies pattern recognition and machine learning algorithms to build predictive models. This chapter explains the two main components of the neural network algorithm – Nodes and Links. Nodes are artificial neurons and Links are the components which connects these nodes. The other topics included in this chapter are

- Kohonen Neural Network
- Steps of algorithm of learning the neural network
- Case studies

**Section 8: Support Vector Machines**

__Support Vector Machines__

Support Vector Machine (SVM) is a supervised machine learning algorithm which is used for analyzing data for classification and regression categories. But this algorithm is mostly used in classification problems. The advantages of SVM are it can be effectively used in high dimensional spaces and it is also memory efficient. The SVM is also versatile and different kernels can be used here. The main disadvantage of SVM is that it produces poor performance if the number of features is more than the number of samples. The other topics included in this section are listed below

- Linear Support Vector Machines
- Non Linear SVM
- Basics of Support Vector Machines
- Calculating the SVM classifier
- How is the optimal hyperplane calculated
- How to implement SVM in Python
- Source code and Explanation
- Tuning the parameters of SVM
- Support Vector Regression
- Pros and Cons of Support Vector Machines
- Practice problem and Case Studies

### FAQ’S General Questions

**What will be the career benefits of Predictive Modeling ?**

Due to the huge amount of data present everywhere the importance of analytics is growing abundantly over the last few years. There is a huge demand for predictive modelers and a large number of organizations are looking for persons with predictive modeling skills and experience.

**How Predictive modeling helps the business organizations ?**

The Predictive modeling offers a lot of benefits to the organization. It helps them to improve their business decisions and which in turn will have a huge impact on their business and its profits. It is a measurable impact.

**What skills are needed to deliver predictive modeling ?**

To become a successful Predictive modeler you should possess domain knowledge. You should be well educated with the analytics tools and technology like SAS, R, STATA and others. You should also have some basic knowledge statistical and machine learning techniques. If you possess all these characteristics then you will become an expert and you will have a good career start in predictive modeling field.

### Testimonials

**Harsha**

This is a really impressive course. It is very simple and an effective course on Predictive modeling. The content structure was great and the flow of the content from one section into another was interconnected which made the topics easy to understand and remember. The practical problems and case studies given in each section gave the confidence to face real time problems in predictive analytics.

**Johnson**

Such a great introductory course on Predictive Modeling. I took this course a few weeks back and I just loved this course. I was new to this concept but still this course made me understand each and every concept clearly. It has given me the confidence to work with predictive modeling like an expert. The course was very interesting as well as informative. It was a rewarding course at a very nominal price. I would definitely recommend this course to others.

**Rosemary**

This is a great course on predictive modeling. This is my first online course and it gave me a very good experience. The content was of good quality and even the complex terms were explained neatly. Really a good course.

Where do our learners come from? |

Professionals from around the world have benefited from eduCBA’s Predictive Modeling Training courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many. |